arXiv subjects

Alessandro Abate

Publications and source records attributed to Alessandro Abate.

At least 163 records · Page 9Linked to original sources

arXiv2019

StocHy: automated verification and synthesis of stochastic processes

StocHy is a software tool for the quantitative analysis of discrete-time stochastic hybrid systems (SHS). StocHy accepts a high-level description of stochastic models and constructs an equivalent SHS model. The tool allows to (i) simulate the SHS evolution over a given time horizon; and to automatically construct formal abstractions of the SHS. Abstractions are then employed for (ii) formal verification or (iii) control (policy, strategy) synthesis. StocHy allows for modular modelling, and has separate simulation, verification and synthesis engines, which are implemented as independent libraries. This allows for libraries to be easily used and for extensions to be easily built. The tool is implemented in C++ and employs manipulations based on vector calculus, the use of sparse matrices, the symbolic construction of probabilistic kernels, and multi-threading. Experiments show StocHy's markedly improved performance when compared to existing abstraction-based approaches: in particular, StocHy beats state-of-the-art tools in terms of precision (abstraction error) and computational effort, and finally attains scalability to large-sized models (12 continuous dimensions). StocHy is available at www.gitlab.com/natchi92/StocHy.

eess.SY↗

arXiv2019

Efficiency through Uncertainty: Scalable Formal Synthesis for Stochastic Hybrid Systems

This work targets the development of an efficient abstraction method for formal analysis and control synthesis of discrete-time stochastic hybrid systems (SHS) with linear dynamics. The focus is on temporal logic specifications, both over finite and infinite time horizons. The framework constructs a finite abstraction as a class of uncertain Markov models known as interval Markov decision process (IMDP). Then, a strategy that maximizes the satisfaction probability of the given specification is synthesized over the IMDP and mapped to the underlying SHS. In contrast to existing formal approaches, which are by and large limited to finite-time properties and rely on conservative over-approximations, we show that the exact abstraction error can be computed as a solution of convex optimization problems and can be embedded into the IMDP abstraction. This is later used in the synthesis step over both finite- and infinite-horizon specifications, mitigating the known state-space explosion problem. Our experimental validation of the new approach compared to existing abstraction-based approaches shows: (i) significant (orders of magnitude) reduction of the abstraction error; (ii) marked speed-ups; and (iii) boosted scalability, allowing in particular to verify models with more than 10 continuous variables.

eess.SY↗

arXiv2018

Temporal logic control of general Markov decision processes by approximate policy refinement

The formal verification and controller synthesis for Markov decision processes that evolve over uncountable state spaces are computationally hard and thus generally rely on the use of approximations. In this work, we consider the correct-by-design control of general Markov decision processes (gMDPs) with respect to temporal logic properties by leveraging approximate probabilistic relations between the original model and its abstraction. We newly work with a robust satisfaction for the construction and verification of control strategies, which allows for both deviations in the outputs of the gMDPs and in the probabilistic transitions. The computation is done over the reduced or abstracted models, such that when a property is robustly satisfied on the abstract model, it is also satisfied on the original model with respect to a refined control strategy.

eess.SY↗

arXiv2018

Maintenance of Smart Buildings using Fault Trees

Timely maintenance is an important means of increasing system dependability and life span. Fault Maintenance trees (FMTs) are an innovative framework incorporating both maintenance strategies and degradation models and serve as a good planning platform for balancing total costs (operational and maintenance) with dependability of a system. In this work, we apply the FMT formalism to a {Smart Building} application and propose a framework that efficiently encodes the FMT into Continuous Time Markov Chains. This allows us to obtain system dependability metrics such as system reliability and mean time to failure, as well as costs of maintenance and failures over time, for different maintenance policies. We illustrate the pertinence of our approach by evaluating various dependability metrics and maintenance strategies of a Heating, Ventilation and Air-Conditioning system.

eess.SY↗

arXiv2018

Experimental Biological Protocols with Formal Semantics

Both experimental and computational biology is becoming increasingly automated. Laboratory experiments are now performed automatically on high-throughput machinery, while computational models are synthesized or inferred automatically from data. However, integration between automated tasks in the process of biological discovery is still lacking, largely due to incompatible or missing formal representations. While theories are expressed formally as computational models, existing languages for encoding and automating experimental protocols often lack formal semantics. This makes it challenging to extract novel understanding by identifying when theory and experimental evidence disagree due to errors in the models or the protocols used to validate them. To address this, we formalize the syntax of a core protocol language, which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols. We present both a deterministic and a stochastic semantics to this language, both defined in terms of hybrid processes. In particular, the stochastic semantics captures uncertainties in equipment tolerances, making it a suitable tool for both experimental and computational biologists. We illustrate how the proposed protocol language can be used for automated verification and synthesis of laboratory experiments on case studies from the fields of chemistry and molecular programming.

cs.PL↗

arXiv2018

Benchmarks for cyber-physical systems: A modular model library for building automation systems (Extended version)

Building Automation Systems (BAS) are exemplars of Cyber-Physical Systems (CPS), incorporating digital control architectures over underlying continuous physical processes. We provide a modular model library for BAS drawn from expertise developed on a real BAS setup. The library allows to build models comprising of either physical quantities or digital control modules.% which are composable. The structure, operation, and dynamics of the model can be complex, incorporating (i) stochasticity, (ii) non-linearities, (iii) numerous continuous variables or discrete states, (iv) various input and output signals, and (v) a large number of possible discrete configurations. The modular composition of BAS components can generate useful CPS benchmarks. We display this use by means of three realistic case studies, where corresponding models are built and engaged with different analysis goals. The benchmarks, the model library and data collected from the BAS setup at the University of Oxford, are kept on-line at https://github.com/natchi92/BASBenchmarks.

eess.SY↗

arXiv2018

Efficient Probabilistic Model Checking of Smart Building Maintenance using Fault Maintenance Trees

Cyber-physical systems, like Smart Buildings and power plants, have to meet high standards, both in terms of reliability and availability. Such metrics are typically evaluated using Fault trees (FTs) and do not consider maintenance strategies which can significantly improve lifespan and reliability. Fault Maintenance trees (FMTs) -- an extension of FTs that also incorporate maintenance and degradation models, are a novel technique that serve as a good planning platform for balancing total costs and dependability of a system. In this work, we apply the FMT formalism to a Smart Building application. We propose a framework for modelling FMTs using probabilistic model checking and present an algorithm for performing abstraction of the FMT in order to reduce the size of its equivalent Continuous Time Markov Chain. This allows us to apply the probabilistic model checking more efficiently. We demonstrate the applicability of our proposed approach by evaluating various dependability metrics and maintenance strategies of a Heating, Ventilation and Air-Conditioning system's FMT.

cs.LO↗

arXiv2017

Unbounded-Time Analysis of Guarded LTI Systems with Inputs by Abstract Acceleration (extended version)

Linear Time Invariant (LTI) systems are ubiquitous in control applications. Unbounded-time reachability analysis that can cope with industrial-scale models with thousands of variables is needed. To tackle this problem, we use abstract acceleration, a method for unbounded-time polyhedral reachability analysis for linear systems. Existing variants of the method are restricted to closed systems, i.e., dynamical models without inputs or non-determinism. In this paper, we present an extension of abstract acceleration to linear loops with inputs, which correspond to discrete-time LTI control systems under guard conditions. The new method relies on a relaxation of the solution of the linear dynamical equation that leads to a precise over-approximation of the set of reachable states, which are evaluated using support functions. In order to increase scalability, we use floating-point computations and ensure soundness by interval arithmetic. Our experiments show that performance increases by several orders of magnitude over alternative approaches in the literature. In turn, this tremendous gain allows us to improve on precision by computing more expensive abstractions. We outperform state-of-the-art tools for unbounded-time analysis of LTI system with inputs in speed as well as in precision.

eess.SY↗

arXiv2017

Automated Experiment Design for Data-Efficient Verification of Parametric Markov Decision Processes

We present a new method for statistical verification of quantitative properties over a partially unknown system with actions, utilising a parameterised model (in this work, a parametric Markov decision process) and data collected from experiments performed on the underlying system. We obtain the confidence that the underlying system satisfies a given property, and show that the method uses data efficiently and thus is robust to the amount of data available. These characteristics are achieved by firstly exploiting parameter synthesis to establish a feasible set of parameters for which the underlying system will satisfy the property; secondly, by actively synthesising experiments to increase amount of information in the collected data that is relevant to the property; and finally propagating this information over the model parameters, obtaining a confidence that reflects our belief whether or not the system parameters lie in the feasible set, thereby solving the verification problem.

cs.LG↗

arXiv2017

Automated Formal Synthesis of Digital Controllers for State-Space Physical Plants

We present a sound and automated approach to synthesize safe digital feedback controllers for physical plants represented as linear, time invariant models. Models are given as dynamical equations with inputs, evolving over a continuous state space and accounting for errors due to the digitalization of signals by the controller. Our approach has two stages, leveraging counterexample guided inductive synthesis (CEGIS) and reachability analysis. CEGIS synthesizes a static feedback controller that stabilizes the system under restrictions given by the safety of the reach space. Safety is verified either via BMC or abstract acceleration; if the verification step fails, we refine the controller by generalizing the counterexample. We synthesize stable and safe controllers for intricate physical plant models from the digital control literature.

eess.SY↗

arXiv2017

On the Relationship between Bisimulation and Trace Equivalence in an Approximate Probabilistic Context (Extended Version)

This work introduces a notion of approximate probabilistic trace equivalence for labelled Markov chains, and relates this new concept to the known notion of approximate probabilistic bisimulation. In particular this work shows that the latter notion induces a tight upper bound on the approximation between finite-horizon traces, as expressed by a total variation distance. As such, this work extends corresponding results for exact notions and analogous results for non-probabilistic models. This bound can be employed to relate the closeness in satisfaction probabilities over bounded linear-time properties, and allows for probabilistic model checking of concrete models via abstractions. The contribution focuses on both finite-state and uncountable-state labelled Markov chains, and claims two main applications: firstly, it allows an upper bound on the trace distance to be decided for finite state systems; secondly, it can be used to synthesise discrete approximations to continuous-state models with arbitrary precision.

cs.LO↗

arXiv2017

Control refinement for discrete-time descriptor systems: a behavioural approach via simulation relations

The analysis of industrial processes, modelled as descriptor systems, is often computationally hard due to the presence of both algebraic couplings and difference equations of high order. In this paper, we introduce a control refinement notion for these descriptor systems that enables analysis and control design over related reduced-order systems. Utilising the behavioural framework, we extend upon the standard hierarchical control refinement for ordinary systems and allow for algebraic couplings inherent to descriptor systems.

eess.SY↗

arXiv2017

Sound and Automated Synthesis of Digital Stabilizing Controllers for Continuous Plants

Modern control is implemented with digital microcontrollers, embedded within a dynamical plant that represents physical components. We present a new algorithm based on counter-example guided inductive synthesis that automates the design of digital controllers that are correct by construction. The synthesis result is sound with respect to the complete range of approximations, including time discretization, quantization effects, and finite-precision arithmetic and its rounding errors. We have implemented our new algorithm in a tool called DSSynth, and are able to automatically generate stable controllers for a set of intricate plant models taken from the literature within minutes.

eess.SY↗

arXiv2016

Symbolic Abstractions of Networked Control Systems

The last decade has witnessed significant attention on networked control systems (NCS) due to their ubiquitous presence in industrial applications, and, in the particular case of wireless NCS, because of their architectural flexibility and low installation and maintenance costs. In wireless NCS the communication between sensors, controllers, and actuators is supported by a communication channel that is likely to introduce variable communication delays, packet losses, limited bandwidth, and other practical non-idealities leading to numerous technical challenges. Although stability properties of NCS have been investigated extensively in the literature, results for NCS under more complex and general objectives, and in particular results dealing with verification or controller synthesis for logical specifications, are much more limited. This work investigates how to address such complex objectives by constructively deriving symbolic models of NCS, while encompassing the mentioned network non-idealities. The obtained abstracted (symbolic) models can then be employed to synthesize hybrid controllers enforcing rich logical specifications over the concrete NCS models. Examples of such general specifications include properties expressed as formulae in linear temporal logic (LTL) or as automata on infinite strings. We thus provide a general synthesis framework that can be flexibly adapted to a number of NCS setups. We illustrate the effectiveness of the results over some case studies.

math.OC↗

arXiv2016

Safety Verification of Output Feedback Controllers for Nonlinear Systems

A high-gain observer is used for a class of feedback linearisable nonlinear systems to synthesize safety-preserving controllers over the observer output. A bound on the distance between trajectories under state and output feedback is derived, and shown to converge to zero as a function of the gain parameter of an observer. We can therefore recover safety properties under output feedback and control saturation constraints by synthesizing a controller as if the full state were available. We specifically design feedback linearising controllers that satisfy certain properties, such as stability, and then construct the associated maximal safety-invariant set, namely the largest set of all initial states that are guaranteed to produce safe trajectories over a given (possibly infinite) time horizon.

eess.SY↗

arXiv2016

Towards Scalable Synthesis of Stochastic Control Systems

Formal control synthesis approaches over stochastic systems have received significant attention in the past few years, in view of their ability to provide provably correct controllers for complex logical specifications in an automated fashion. Examples of complex specifications of interest include properties expressed as formulae in linear temporal logic (LTL) or as automata on infinite strings. A general methodology to synthesize controllers for such properties resorts to symbolic abstractions of the given stochastic systems. Symbolic models are discrete abstractions of the given concrete systems with the property that a controller designed on the abstraction can be refined (or implemented) into a controller on the original system. Although the recent development of techniques for the construction of symbolic models has been quite encouraging, the general goal of formal synthesis over stochastic control systems is by no means solved. A fundamental issue with the existing techniques is the known "curse of dimensionality," which is due to the need to discretize state and input sets and that results in an exponential complexity over the number of state and input variables in the concrete system. In this work we propose a novel abstraction technique for incrementally stable stochastic control systems, which does not require state-space discretization but only input set discretization, and that can be potentially more efficient (and thus scalable) than existing approaches. We elucidate the effectiveness of the proposed approach by synthesizing a schedule for the coordination of two traffic lights under some safety and fairness requirements for a road traffic model. Further we argue that this 5-dimensional linear stochastic control system cannot be studied with existing approaches based on state-space discretization due to the very large number of generated discrete states.

eess.SY↗

arXiv2015

Observer-based correct-by-design controller synthesis

Current state-of-the-art correct-by-design controllers are designed for full-state measurable systems. This work first extends the applicability of correct-by-design controllers to partially observable LTI systems. Leveraging 2nd order bounds we give a design method that has a quantifiable robustness to probabilistic disturbances on state transitions and on output measurements. In a case study from smart buildings we evaluate the new output-based correct-by-design controller on a physical system with limited sensor information.

eess.SY↗

arXiv2015

Data-driven and Model-based Verification: a Bayesian Identification Approach

This work develops a measurement-driven and model-based formal verification approach, applicable to systems with partly unknown dynamics. We provide a principled method, grounded on reachability analysis and on Bayesian inference, to compute the confidence that a physical system driven by external inputs and accessed under noisy measurements, verifies a temporal logic property. A case study is discussed, where we investigate the bounded- and unbounded-time safety of a partly unknown linear time invariant system.

eess.SY↗